Estimating variances in noise is of key importance in many image processing applications, such as filtering, enhancement, quality assessment, and detecting forgery. For the existing detection methods that are based on inconsistencies in noise, the conventional approach is to estimate the noise variance of each region first and then identify the regions with extremely higher or lower variance as splicing regions. However, due to the impossibility of completely separating image noise and inherent texture, inevitably, each estimate is overestimated, especially for regions that have more complex textures. In this paper, we consider the issue that the estimation of the noise of each region frequently is inaccurate due to the complexity of the texture of the region. Based on this consideration and motivated by the scoring strategy-based, object-proposal technique, an approach that incorporates the inhomogeneity scoring strategy is proposed to provide a more convincing result to expose image-splicing manipulations. Specifically, first, the image is segmented into small patches, and the noise variance of each patch is computed by using the kurtosis concentration-based pixel-level noise estimation method. Then, the inhomogeneity score is computed using the spectral residual-based saliency measurement method. After using a linear equation fitting based on the estimated sample of variance and the inhomogeneity score of each patch, the suspicious region can be identified by seeking the conjunct patches that are out of the linear constraint. The experimental results demonstrated the efficacy and robustness of the proposed method.
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We would like to thank Drs. Siwei Lyu and Hui Zeng for kindly sharing the codecs of their work. This work was supported in part by the National Natural Science Foundation of China (61702332, 61672354, 61562007), Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security (MIMS16-03), the PAPD Fund, and the CICAEET Fund.
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